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Machine Learning, Misinformation, and Citizen Science

Yee, Adrian K. (2023) Machine Learning, Misinformation, and Citizen Science. [Preprint]

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Abstract

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Yee, Adrian K.adriankyle.yee@mail.utoronto.ca0000-0002-41709257
Additional Information: Forthcoming in European Journal for Philosophy of Science
Keywords: misinformation, machine learning, citizen science, social epistemology, measurement, construct validity
Subjects: General Issues > Data
Specific Sciences > Artificial Intelligence
General Issues > Feminist Approaches
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Science and Policy
General Issues > Social Epistemology of Science
General Issues > Theory/Observation
General Issues > Values In Science
Depositing User: Adrian K. Yee
Date Deposited: 03 Aug 2023 14:24
Last Modified: 03 Aug 2023 14:24
Item ID: 22366
Subjects: General Issues > Data
Specific Sciences > Artificial Intelligence
General Issues > Feminist Approaches
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
General Issues > Science and Policy
General Issues > Social Epistemology of Science
General Issues > Theory/Observation
General Issues > Values In Science
Date: 2023
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/22366

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